Data tracking is becoming more and more important as technology evolves. A global data explosion is generating almost 2.5 quintillion bytes of data today, and unless that data is organized properly, it is useless. The collection of meaningful market data has become a critical component of maintaining consistency in businesses today. A company can make the right decision by organizing a massive amount of raw data with the right data analytic tool and a professional data analyst. You can check out the Big Data Certification Online to have an in-depth idea about big data tools and technologies to prepare for a job in the domain.
To get your business in the direction you want, you need to choose the right tools for big data analysis based on your business goals, needs, and variety. This article will discuss some best big data tools, big data reporting tools and modern data analytic tools in big data.
What Is Big Data Analytics?
An organization can make informed decisions based on a big data analytics platform, which works by uncovering patterns, correlations, customer preferences and market trends hidden in the data.
Technologies and techniques for data analytics enable organizations to gather new information and analyze data sets on a broad scale. Answering business intelligence (BI) questions relates to the operation and performance of a business. Big data tools are used to perform predictive modeling, statistical algorithms and even what-if analyses.
Some important big data processing platforms are:
- Microsoft Azure.
- Cloudera.
- Sisense.
- Collibra.
- Tableau.
Why Is Big Data Analytics Important?
Data analysts can be instrumental in helping organizations improve the way they make business-related decisions by using software and big data analytics framework that is aimed at analyzing big data. As a result, there may be an increase in marketing effectiveness, the possibility of new revenue opportunities, the ability to offer personalized services to customers, and an increase in cost efficiency. It is possible to gain competitive advantages over rivals by implementing these benefits as part of an effective strategy. The application of big data analysis can assist businesses in making better business decisions by analyzing large amounts of data and uncovering hidden patterns. Real-time analytics platforms in big data apply logic and math to gain faster insights into data, resulting in a more streamlined and informed decision-making process.
Some open-source technology for big data analytics are:
- Hadoop.
- Apache Spark.
- Cassandra.
- MongoDB.
- HPCC.
- Apache Storm.
- Apache SAMOA.
- Atlas.ti.
Evolution of Big Data Analytics
In the past few years, the concept of big data has become increasingly popular; most businesses are now recognizing that if they capture all the data, which streams into their business, they can apply analytics and extract significant value from it. Even in the 1950s, decades before the term "big data" were uttered, businesses were already using basic analytics, essentially numerical data, in a spreadsheet to uncover insights and trends that could be applied to other areas of business.
But the most notable benefits of big data analytics are the speed and efficiency that are brought to the table by it. A few years ago, a company would gather information, run big data analytics software on that data, and unearth information that could help the company make decisions in the future. However, today, a company can simply tap into that data and identify insights that can help the company make decisions. Work at a faster pace and keep your agility while doing so. It ensures that organizations have an edge over their competitors that they did not have in the past. Let's check some of the best big data analytics tools and free big data analytics tools.
Open-source big data analytics tools are intended to be publicly accessible and are typically managed and maintained by organizations with a specific mission. Letās check some important big data processing tools. Let's check some big data analytics tools examples and software used in big data analytics. Listed below are the top and the most popular tools for big data analytics:
1. APACHE Hadoop
Big data is being processed and stored using this Java-based open-source platform, and data can be processed efficiently and in parallel thanks to the cluster system. Data from one server can be processed by multiple structured and unstructured computers, and users of Hadoop can also access it across multiple platforms. Amazon, Microsoft, IBM, and other tech giants use it today as one of the best tools for big data analysis.
Features:
- Businesses can use this storage solution for free, and it is an efficient one.
- It can be installed on a bunch of disks or a JBOD of commodity hardware.
- The Hadoop Distributed File System (HDFS) provides quick access.
- Easily scales up to a large amount of data when it is distributed in small chunks.
- Easy to implement with MySQL, JSON, and highly flexible.
2. Cassandra
Data sets can be retrieved in large quantities using APACHE Cassandra, a distributed database with no SQL engine. A number of tech companies have praised it for its high availability and scalability without sacrificing speed or performance without compromising speed. It can handle petabytes of resources with almost no downtime and delivers thousands of operations every second. A public version of this best big data tool was created in 2008 by Facebook.
Features:
- With Cassandra, you can store data quickly and process it efficiently on efficient commodity hardware.
- Data can be structured, semi-structured, or unstructured, and users can change the data according to their requirements.
- Distributing data across multiple data centers is easy with replication.
- As soon as a node fails, it will be replaced immediately.
3. Qubole
Using ad-hoc analysis in machine learning, it fetches data from a value chain using open-source technology for big data analytics. Qubole provides end-to-end services in moving data pipelines with reduced time and effort. Configure Azure, AWS, and Google Cloud services simultaneously. As a result, cloud computing costs are also reduced by 50%.
Features:
- In order to target more acquisitions, Qubole offers predictive analysis.
- Multi-source data can be migrated to one location through this tool.
- Users can view real-time insights into their systems when it monitors their systems.
4. Xplenty
Using minimal code allows you to build a data pipeline. Its sales, marketing, and support solutions cover a broad spectrum of needs. In addition to providing ETL and ELT solutions, it features an interactive graphical interface. With Xplenty, you'll spend less on hardware and software and receive support via chat, email, telephonic and virtual meetings. Data can be processed for the application of big data analysis over the cloud and segregated using Xplenty.
Features:
- Integrated apps can be deployed on-premises and in the cloud.
- Regular verification of algorithms and certificates can be performed on the platform, along with SSL/TSL encryption.
- Databases, warehouses, and salesforce can receive and process data.
5. Spark
Data processing and numerous tasks can also be done on a large-scale using Apache Spark. With the help of tools for big data, data can also be processed via multiple computers. Due to its easy-to-use APIs and its ability to handle multi-petabytes of data, it is widely used among data analysts. Spark is highly suitable for ML and AI today, which is why big tech giants are moving towards it now.
Features:
- Users can choose the language they wish to run in.
- Streaming can be handled by Spark using Spark Streaming.
6. MongoDB
This free, open-source platform, which came into the limelight in 2010, is a document-oriented (NoSQL) database that is used to store a large amount of information in a structured manner. As a result of MongoDB's support for multiple programming languages, such as Jscript, Python, and Ruby, it is extremely popular among developers.
Features:
- The backup function can be called back after writing or reading data from the master.
- Documents can be stored in a schema-less database.
- The Mongo database makes storing files easy without disrupting the stack.
7. Apache Storm
Small companies, especially those that do not have the resources for big data analytics, use storms, which are robust and user-friendly tools. Storm does not have any language barriers (programming) and can support any of them. With fault tolerance and horizontal scalability, it was designed to handle a pool of large data. Since Storm has a distributed real-time big data processing system, it leads the pack when it comes to real-time data processing. APACHE Storm is used in many tech giants' systems today. NaviSite, Twitter, and Zendesk are among the most well-known.
Features:
- With APACHE Storm, a single node is capable of processing up to 1 million messages per second.
- Even if a node is disconnected, Storm still processes the data.
8. SAS
It is one of the best tools used by data analysts today to create statistical models, and data scientists can manage data from several sources and mine, extract, or update it using it. Data can be accessed in SAS tables or Excel worksheets using Statistics Analytical System (SAS). In addition to that, SAS has also introduced new tools used in big data and products to get a stronger grip on artificial intelligence & machine learning.
Features:
- Data can be read from any format and is compatible with many programming languages, including SQL.
- Non-programmers will appreciate its easy-to-learn syntax as well as its vast libraries.
9. Data Pine
Since 2012, Datapine has been providing analytics for business intelligence (Berlin, Germany). Since its introduction, it has gained considerable popularity in a number of countries, especially in small- and medium-sized companies that need to extract data for monitoring purposes. Thanks to the enhanced user interface, anyone can access the data according to their requirements and choose between four price brackets, starting at $249 per month. Dashboards are available by function, industry, and platform.
Features:
- Through the use of historical and current data, datapine provides forecasting and predictive analytics.
- Our BI tools and AI assistants are designed to cut down the manual chase.
10. Rapid Miner
Its purpose is to automate the design of data analytics workflows using visual tools. With this platform, users do not need to code in order to segregate data. Education technology, training, research, etc., are among the industries heavily using it today. Although it's open source, it only supports 10000 data rows and one logical processor. ML models can be deployed to the web or mobile (only when the user interface is ready for real-time data collection) with the assistance of Rapid Miner.
Features:
- Various file types (SAS, ARFF, etc.) are accessible via URL.
- For better evaluation, Rapid Miner allows multiple results to be displayed in history.
11. Tableau Public
Tableau Public is a free online platform that allows users to create and share visualizations and data-driven stories. It is a great platform for data exploration and communication.
12. Integrate.io
Integrate.io is an all-in-one solution for businesses to connect their data and applications. It offers a cloud-based integration platform that helps businesses automate their data integration processes.
13. Google Fusion Tables
Google Fusion Tables is a web service that allows users to upload, merge, and visualize data tables. It is a part of the Google Drive suite of products.
14. Atlas.ti
Atlas.ti is a powerful qualitative data analysis software that offers a wide range of features to support your research. It helps you to organize, code, and analyze your data, as well as to create visualizations and reports.
Big data analytics tools and methods help organizations make sense of their data by providing a way to visualize and interact with it. These tools of big data analytics can be used to identify patterns, trends, and relationships and to make predictions about future events.
Big Data analytics is the entire process of obtaining, organizing, analyzing, and identifying patterns in large data sets (often referred to as Big Data) to make business choices. Big Data analytics is a procedure that aids businesses in better understanding the information contained in data sets. The knowledge that results from studying the data is normally available to the man who works as an analyst with big data. Data analytics tools in big data includes a variety of tools that can be used to enhance the data analysis process. These tools include data analysis, data purification, data mining, data visualization, data integration, data storage, and management.
Very High-Performance Analytics is required for the big data analytics process. As a result, to evaluate such a large amount of data, specific software tools are needed for applications such as predictive analytics, data mining, text mining, forecasting, and data optimization.
There are a few factors to consider when selecting the best big data analytics tool for your organization.
- The first is the type of data you have, which will determine the tool you need. If you have structured data, you will need a different tool than if you have unstructured data.
- The second factor is the size of your data, which will determine the tool best suited for your organization. If you have a lot of data, you will need a different tool than if you have a smaller amount of data.
- The third factor is the level of analysis you need. If you need a more sophisticated analysis, you will need a different tool than if you need a less sophisticated analysis.
- The fourth factor is the budget you have which will determine the type of tool you can afford.
- Tools and technologies for big data analytics can be overwhelming. Here are four key considerations for choosing the right tools for your organization:
- Tools in big data analytics should be able to handle the volume, variety, and velocity of data, which may be coming from social media, sensors, transactions, and more.
- The tools should be able to process data in real-time or near-real-time. This way, you can make decisions based on the most up-to-date information.
- The tools should be able to provide insights at the individual level, which means being able to drill down into data to understand what is happening at a granular level.
- The tools should be able to integrate with other systems. This way, you can get a complete picture of what is happening across your organization.
- Healthcare: Big data analytics technologies and tools are being used in healthcare to predict patient outcomes, identify at-risk patients, and improve population health.
- Retail: Big data analytics tools are being used by retailers to improve customer experience, target marketing campaigns, and prevent fraud.
- Manufacturing: Big data analytics tools are being used in manufacturing to improve quality control, reduce downtime, and optimize production processes.
- Banking: Real time big data analytics tools are being used by banks to detect fraudulent activities, prevent money laundering, and improve customer service.
- Government: Big data analytics tools are being used by government agencies to improve public services, combat fraud and corruption, and better understand citizen needs.
There are several limitations to big data analytics tools, including:
- They can be expensive and require a lot of resources to implement.
- They can be complex to use and require skilled staff to get the most out of them.
- They can require a lot of data to be effective, which can be a challenge to collect.
- They can be slow and may not be able to keep up with rapidly changing data.
- They can produce biased results, depending on how they are configured.
Unlock the Power of Data Science: Discover a World of Possibilities! Explore the eligibility criteria for data science and embark on a journey of innovation and success. Join us today!
Conclusion
So, you now must have a clear view of the various big data predictive analytics tools. With these tools, an individual or a business can improve the way they make business-related decisions. However, to understand more about big data analytics tools and how to use big data analytics, you can enroll in the KnowledgeHut Big Data Certification Online Course. This course will help you to develop a robust skill set while working with the most powerful Big Data tools and technologies to help you advance your career in big data.
